The gut microecology of the two sympatric gerbil species varies with diet

The gut microecology of the two sympatric gerbil species varies with diet | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The gut microecology of the two sympatric gerbil species varies with diet Dongyang Chu, Nan Liu, Qingxuan Liu, Shuai Yuan, Heping Fu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7587564/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The gut microbiota of mammals providesa range of benefits to their hosts; however, knowledge of interspecific differences in gut microecology remains limited. In this study, we employed 16S rRNA sequencing combined with metagenomic functional prediction to comparethe gut microbial composition and functional adaptability of two sympatrically distributed gerbil species with distinct diets—the herbivorous Rhombomys opimus ( RO) and the omnivorous Meriones meridianu s ( MM ). Our findings revealed that MM possessed greater microbial diversity, whereas RO exhibited a significant enrichment of norank_f__Muribaculaceae, a taxon associated with fibre degradation, along with a greaterabundance of genes involved in complex fibre breakdown. Notably, Lachnospiraceae_NK4A136_group and Desulfovibrio, which were markedly enriched in MM, may play pivotal roles in maintaining gut health and enhancing the efficiency of chitin degradation. Functional network analysis demonstrated that the cellulose-degrading gene networks in both gerbil species were predominantly synergistic; however, compared with RO, MM displayed significantly greater abundances of genes related to monosaccharide and chitin degradation. Further analysis indicated that the monosaccharide- and chitin-degrading gene networks of MM exhibited cooperative interaction patterns, whereas in RO, these networks were primarily antagonistic. This disparity reflects the heightened adaptability of MM to starch derived from plant seeds and chitin from insect exoskeletons, potentially linked to its omnivorous feeding habits and digestive constraints associated with smaller body size. Overall, this study advances our understanding of gut microecological adaptation mechanisms in rodents fed differentdiets and provides a valuable foundation for future research into the microbial ecology of wild rodents. Sympatric coexistence gerbils dietary habits gut microbiome Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction The gut microbiome of mammals constitutes a highly complex and biodiverse ecosystem, with the total number of genes it encodes estimated to be approximately 150 times greater than that of the host’s own genome [1] , and is therefore often referred to as the host’s “second genome”. The structure and function of this microbial community are governed by a range of multidimensional factors, including intrinsic determinants such as the host’s genetic background, taxonomy, and developmental stage, as well as extrinsic influences such as dietary composition, geographic habitat, and social behaviour [2] . Among these numerous factors, host phylogenetic relationships and dietary strategies are widely recognized as two principal driving forces that shape the composition and functional evolution of the gut microbiota [3] . Phylogenetic relationships capture the conservative selection of microbial communities over the course of the host’s long-term evolutionary history [4] , whereas diet exerts direct selective pressures through the daily intake of nutrients, thus driving the dynamic adaptation and functional differentiation of the microbial community [5] . The extensive metabolic diversity within gut microbial communities endows the host with a broad array of distinctive functions and directly contributes to its physiological activities and energy metabolism. Consequently, there is an increasing consensus that the microbiome may serve as a key mediator enabling the host to achieve rapid ecological adaptation and evolutionary diversification [6] . Among these functions, one of particular importance is the ability of microbes to degrade complex biopolymers that the host itself cannot process, thus expanding the range of nutrient resources available to the host. For instance, structural components of plant cell walls—such as cellulose and hemicellulose—are carbohydrate polymers composed of β-1,4-glycosidic bonds that cannot be hydrolysed by the host’s endogenous enzymatic systems [7] . Thus, most herbivorous mammals depend on gut microbes to perform anaerobic fermentation, transforming these otherwise indigestible polysaccharides into absorbable metabolites, such as short-chain fatty acids, which constitute important sources for maintaining the host’s energy balance. Another biopolymer of notable significance is chitin. As the second most abundant polysaccharide in nature [8], chitin is widely distributed in the exoskeletons of arthropods and the cell walls of fungi and is a linear polymer composed of N-acetylglucosamine units linked by β-1,4 bonds. It is a substantial dietary component for many insectivorous and omnivorous vertebrates. Research on mammals specialized in consuming ants and termites—such as Myrmecophaga tridactyla , Manis pentadactyla , and Orycteropus afer —has revealed that despite their considerable phylogenetic distance, these hosts have evolved strikingly convergent gut microbial communities [9] , suggesting that microbes may play a pivotal role in facilitating adaptation to chitin-rich diets. However, little is currently known about whether such microbial convergence is widespread among other insectivorous taxa, whether these communities indeed increase chitin digestion efficiency, and which specific microbial taxa are involved and play critical roles in this process [10] . In particular, for omnivorous species that are not yet specialized that regularly ingest chitin, whether their microbiomes undergo similar adaptive modifications remains to be systematically investigated. Rodents serve as excellent models for investigating fundamental questions in host–microbe interactions. As the most species-rich order among mammals, they exhibit diverse ecological adaptations, occupy a broad range of habitats, and display notable dietary variation [11] . Consequently, rodents have become a widely utilized model system in comparative biology [12] , particularly within the realm of host–microbe interactions [13] . Both RO and MM belong to the order Rodentia and the family Muridae and are commonly found in the desert regions of Central Asia. The former is the sole representative of the genus Rhombomys, whereas the latter is classified under the genus Meriones. Both species are widely distributed across northern China [14] . Notably, in certain habitats, their ranges frequently overlap. In regions where RO is present, MM often occurs as a sympatric species, resulting in a stable pattern of coexistence [15] . Research has indicated that this coexistence is largely facilitated by pronounced trophic niche differentiation: RO is a specialized herbivore with a restricted diet, feeding primarily on moisture-rich green stems and leaves of plants; in contrast, MM is a typical omnivore, consuming plant matter alongside a substantial proportion of insects and seeds [16] . This marked dietary divergence mitigates direct competition for limited resources and constitutes a key niche differentiation strategy that underpins their long-term stable coexistence within overlapping distribution areas. To determine whether the aforementioned dietary divergence is reflected in the gut microbiome structure, which is closely associated with digestive function, this study employed 16S rRNA gene high-throughput sequencing to characterize the gut microbial communities of two sympatric gerbil species inhabiting the same region but adopting distinct feeding strategies. Predictive metagenomics (PICRUSt2) was further applied to infer their potential functional capacities. On the basis of the documented dietary differences between the two species, we formulated the following hypotheses. (i) The two gerbil species will display markedly distinct gut microbiome structures; (ii) The microbial communities, in terms of both taxonomic composition and predicted functions, will be enriched with microbial taxa and functional gene sets associated with the decomposition and metabolism of the principal components of their natural diets (e.g., plant cellulose for RO and chitin for MM). Methods Rodent collection In 2024, two rodent species were collected from the desert region in the southern part of Alxa Left Banner, Alxa League, Inner Mongolia Autonomous Region, China (37.887497°N, 105.381715°E). Within the sampling area, live traps baited with fresh peanuts and carrots were employed to capture the rodents. The traps were set in the early morning and inspected in the evening, as well as the following morning. Previous research has indicated that short-term bait consumption in live traps does not significantly influence the gut microbiome of rodents [17] . A total of 20 rodents were captured, comprising 10 RO and 10 MM rodents (Table 1 ). We collected cecal content samples from live-captured rodents. All samples were obtained under sterile conditions, placed in 2 mL sterile cryotubes, promptly frozen in liquid nitrogen, and subsequently stored at -80°C in an ultra-low temperature environment. We euthanized RO and MM using inhalation overdose of isoflurane, with 3.5% isoflurane used for induction. Euthanasia of RO and MM was performed by cervical dislocation after confirmation of unconsciousness. For small rodents weighing less than 300 g, cervical dislocation was carried out by experienced personnel. This method was chosen due to its efficiency and minimal distress to the animal. Our study was approved by the Ethics Committee of Inner Mongolia Agricultural University under the approval number NND2017012. DNA Extraction Total DNA was extracted from the caecal contents using the FastDNA® Spin Kit for Soil, in accordance with the manufacturer’s instructions. Caecal contents were selected because the caecum serves as the primary site of food fermentation in rodents and is characterized by high microbial density and activity [18] . The extracted DNA was stored on dry ice and subsequently transported to Shanghai Majorbio Bio-Pharm Technology Co., Ltd. The upstream primer 338F (ACTCCTACGGGAGGCAGCAG) and downstream primer 806R (GGACTACHVGGGTWTCTAAT) were used to amplify the V3–V4 region of the bacterial 16S rRNA gene via PCR [19] , after which the resulting amplicons were pooled and purified. Library preparation was conducted using the NEXTFLEX Rapid DNA-Seq Kit, and sequencing was performed to generate 2 × 300 base-pair paired-end reads. Detailed descriptions of the amplification, library preparation, and sequencing procedures are provided in the Supplementary Materials (Supplementary File 1). Sequence reads have been deposited in the NCBI database under accession number (PRJNA1274967). Quality control of the raw sequencing data was conducted using fastp ( https://github.com/OpenGene/fastp , version 0.20.0) [20] , whereas FLASH ( http://www.cbcb.umd.edu/software/flash , version 1.2.7) was employed for read merging [21] . Bases with quality scores below 20 at the ends of the reads were removed. A 50 bp sliding window was applied, and if the average quality within the window fell below 20, bases from the start of the window to the 3′ end were trimmed. Reads shorter than 50 bp after quality control, as well as those containing N bases, were discarded. Paired-end reads were merged into single sequences on the basis of overlapping regions, requiring a minimum overlap of 10 bp. A maximum mismatch rate of 0.2 was permitted within the overlap region, and sequences not meeting these criteria were excluded. Samples were identified according to barcodes and primers at both ends of the sequences, with read orientations adjusted accordingly. Zero mismatches were allowed for the barcodes, and up to two mismatches were permitted for the primers. OTU clustering at 97% sequence similarity was performed using UPARSE ( http://drive5.com/uparse/ , version 7.1). Reads identified as originating from archaea, chloroplasts, or mitochondria were removed from further analysis [22] . Taxonomic classification of each sequence was carried out using the RDP classifier ( http://rdp.cme.msu.edu/ , version 2.2) against the Silva 16S rRNA database (v138), with a confidence threshold of 70% [23] . Statistical analysis We used QIIME2 software to generate microbial abundance tables across various taxonomic levels, clustered bacterial OTUs using Uparse software with a 97% sequence similarity threshold, and performed statistical analyses of OTUs using Usearch software to determine the composition and quantity of shared and unique OTUs in the gut microbiota of Rhombomys opimus and Meriones unguiculatus . To compare gut microbial community diversity between the two gerbil species, we first calculated alpha diversity indices of the gut microbial communities using Mothur software and then applied Kruskal–Wallis tests to assess differences in alpha diversity, followed by post hoc tests to further identify significant intergroup differences. To subsequently examine differences in the gut microbial community composition, we computed pairwise community dissimilarities for all the samples on the basis of the Bray–Curtis distance [24] , unweighted UniFrac distance, and weighted UniFrac distance [25] and compared the compositional characteristics of the gut microbiota between the two species. Concurrently, nonmetric multidimensional scaling (NMDS) was employed to construct a visual representation, providing an intuitive depiction of similarities and differences among gut microbial communities from different samples. To further validate the microbiome changes (including community dispersion and compositional differences), we adopted two analytical approaches: (1) the PERMDISP test (with 999 permutations) in QIIME2 to evaluate the homogeneity of samples within groups in relation to community dispersion and (2) the calculation of Bray–Curtis distances between paired samples within the same species (intraspecific), followed by Kruskal–Wallis tests with post hoc multiple comparisons to assess the extent of intraspecific community variation. To assess the composition of the bacterial communities in the two gerbil groups, we employed the taxa-barplot tool in QIIME2 to visualize taxonomic abundance. To evaluate differences in bacterial communities between the groups, we used the LEfSe function in STAMP software to identify bacterial taxa exhibiting significantly different abundances at the genus level. Prior to conducting the differential analysis, we excluded bacterial taxa whose read counts were less than 20 to eliminate rare taxa [26] , and the analysis was primarily performed using default parameters. We employed the PICRUSt2 plugin in QIIME 2 to generate predicted metagenomic functional profiles on the basis of 16S rRNA sequencing data [27] . In this process, all OTUs with a nearest sequenced taxon index (NSTI) value exceeding 2.0 were excluded. By acknowledging the potential limitations inherent in predictive metagenomic analysis, we prioritized pre‑established hypotheses and concentrated on assessing the predicted abundance of functional categories associated with biopolymer degradation. We linked the glycoside hydrolase (GH) subset from the Carbohydrate-Active enZymes (CAZy) database with the KEGG Orthology (KO) database [28] to identify metabolic pathways relevant to our functional predictions. We examined genes involved in the digestion of simple sugars, including α-glucosidase (K01187), oligosaccharide-1,6-glucosidase (K01182), α-amylase (K07405), maltose-6’-phosphate glucosidase (K01232), and α-amylase (K01176). To assess the microbial capacity for metabolizing complex fibres, we also analysed the predicted abundances of genes associated with complex fibre degradation, such as β-glucosidase (K05349), endoglucanase (K01179), endo-1,4-β-xylanase (K01181), β-glucosidase (K05350), and xylan-1,4-β-xylosidase (K01198), by KEGG Orthology. Furthermore, we compared the relative abundances of multiple KEGG Orthology genes linked to chitin degradation [29] , including chitinase (K01183), chitosanase (K01233), phospho-chitobiase (K01222), and chitin disaccharide deacetylase (K03478). The predicted abundances of these genes in the two gerbil groups were compared using the Kruskal–Wallis test, followed by post hoc analysis. An interaction network map of bacterial OTUs (top 500 in abundance) from RO and MM associated with cellulose, monosaccharide, and chitin degradation genes was generated using the “psych” package in R v4.5.1 and Gephi v0.9.2 [30] . The network topology features were subsequently calculated with Gephi v0.9.2. Table 1 Characteristics of the study species Species Taxonomicfamily Group Samplesize (n) ♂: ♀ Average weight (g) Diet Rhombomys opimus Muridae RO 5 : 5 164.5 Plant foliage [16] Meriones meridianus Muridae MM 5 : 5 62.3 Plant foliage, insects, seeds [16] (Note: Based on the dietary composition of the two gerbil species, we categorized them as herbivorous (RO), and omnivorous (MM), the same below). Results This study examined the microbial composition of the caecal contents of two sympatric gerbil species. A total of 20 samples were analysed, with the number of valid sequences per sample ranging from 29,592 to 36,472 and an average of 33,770 valid sequences. At the 97% sequence similarity threshold, 5,562 core OTUs were identified. MM had 4,073 shared OTUs and 2,715 unique OTUs, whereas RO had 2,747 shared OTUs and 1,489 unique OTUs (Fig. 1a). These findings suggest that the intestinal tracts of the two species contain a substantial proportion of shared bacterial taxa, along with distinct microbial communities unique to each species. Rarefaction curves indicated that the estimates of species richness approached a plateau and were unbiased, implying that the current sequencing depth adequately captured the vast majority of the bacterial diversity present in the samples, thus supporting its suitability for subsequent data analyses (Fig. 1b). We assessed the alpha diversity indices of the gut microbial communities of two sympatric gerbil species using the Chao1 and Sobs indices to evaluate differences in species richness. The results indicated that the omnivorous MM resulted in higher gut microbial alpha diversity, whereas the herbivorous RO resulted in lower alpha diversity. Nevertheless, statistical analyses revealed that these differences were not significant (Chao1 index: P = 0.1595; Sobs index: P = 0.2484) (Fig. 2 a, b). Individual rodents displayed distinct microbiota compositions. ANOSIM analysis was employed to assess intra- and intergroup differences between the two gerbil species, revealing that the omnivorous MM exhibited greater interindividual microbiota variation than the herbivorous RO did. Furthermore, interspecific differences were markedly greater than intraspecific differences (R = 0.6089, P = 0.001; Fig. 3 a). These findings were corroborated by NMDS analysis, which clearly revealed species-level clustering of the microbiota in the two gerbil species (stress = 0.08, P = 0.001; Fig. 3 b). PERMANOVA analysis further confirmed that host species significantly influenced microbiome structure (F = 18.25, P = 0.001). Collectively, these results indicate that the gut microbiota structures of the two gerbil species differ substantially. We examined the bacterial composition of the gut microbiota of the two gerbil species at the phylum level and identified distinct abundance patterns. In MM, the gut microbiota was predominantly composed of Firmicutes (RO: 35%; MM: 65%), whereas in RO, Bacteroidota prevailed (RO: 63%; MM: 37%). Desulfobacterota and Campilobacterota were markedly enriched in the gut of the omnivorous MM. In contrast, Patescibacteria, Verrucomicrobiota, Actinobacteriota, Proteobacteria, Cyanobacteria, and Spirochaetota were more abundant in the gut of the herbivorous RO (Fig. 4 ). At the genus level, we conducted a LefSe analysis (LDA > 3) on the gut microbiota of the two gerbil species. The results indicated that each species possessed its own uniquely enriched genera. In RO, the significantly enriched genera included norank_f__Muribaculaceae (specializing in the degradation of complex polysaccharides such as plant fibres) [31] , Weissella , Monoglobus , Candidatus_Saccharimonas , norank_o__Clostridia_UCG-014 , Enterococcus , norank_o__RF39 , Alcanivorax , and Dubosiella . In contrast, the gut microbiota of MM was significantly enriched in Lactobacillus (a genus of bacteria capable of lactic acid fermentation, converting carbohydrates into lactic acid) [32] , Lachnospiraceae_NK4A136_group , Desulfovibrio (a sulphate-reducing bacterium and efficient hydrogen scavenger) [33] , norank_f__Lachnospiraceae , Eubacterium_siraeum_group , Helicobacter , Lachnospiraceae_UCG-006 , Colidextribacter , GCA-900066575 , and Lachnoclostridium . When the microbial OTUs identified in our study were compared with the reference genomes in the PICRUSt2 database, the nearest sequenced taxon index (NSTI) for all 5,562 OTUs was below the threshold of 2.0; consequently, none were excluded from the subsequent PICRUSt2 functional prediction analysis (Supplementary File 2). The NSTI values across all the OTUs were generally low (mean ± standard deviation: 0.321 ± 0.194; median: 0.279). A significant difference in the weighted mean NSTI values was observed between the two gerbil species (Kruskal–Wallis test: H = 22.182, P < 0.001). The NSTI values obtained in this study are consistent with those reported in human microbiome research, suggesting that metagenomic functional predictions based on 16S rRNA gene sequencing data are reliable. Functional prediction analysis revealed that the predicted abundance of fibre degradation-related genes in the microbiota of the herbivorous RO was markedly greater, whereas it was substantially lower in the omnivorous MM (Fig. 6 a, b; Kruskal–Wallis test: H = 14.29, P < 0.001). Conversely, the predicted abundance of genes associated with monosaccharide digestion was greater in the omnivorous MM and lower in the herbivorous RO (Fig. 6 c, d; H = 14.29, P < 0.001). In both gerbil species, the most prevalent gene linked to complex fibre metabolism was β-glucosidase (K05349) (Fig. 6 b), whereas the most prevalent gene associated with monosaccharide digestion was α-glucosidase (K01187) (Fig. 6 d) (Supplementary File 3). In this study, Spearman correlation analyses were performed between the 500 most abundant bacterial OTUs in the intestinal tracts of the two gerbil species and genes associated with cellulose and monosaccharide degradation, respectively.. In RO, 114 OTUs were significantly correlated with cellulose-degrading genes (Table 2 ), whereas 126 OTUs were significantly correlated with monosaccharide-degrading genes (Table 3 ). Within the cellulose-degrading gene network of RO, 88 edges represented positive correlations (70.97%), and 36 represented negative correlations (29.03%) (Table 2 , Fig. 7 a), yielding a positive-to-negative edge ratio of 2.44 and indicating a predominance of synergistic interactions. In the MM cellulose-degrading gene network, 89 positively correlated edges (61.38%) and 56 negatively correlated edges (38.62%) were observed (Table 2 , Fig. 7 b), resulting in a positive-to-negative edge ratio of 1.59, likewise indicating a predominance of synergistic interactions, although the degree of synergy was lower than that observed in RO. In the monosaccharide-degrading gene network of RO, 59 positively correlated edges (45.38%) and 71 negatively correlated edges (54.62%) were identified (Table 3 , Fig. 7 c), resulting in a positive-to-negative edge ratio of 0.831, suggesting that antagonistic interactions predominated between monosaccharide-degrading genes and bacterial OTUs. Conversely, the monosaccharide-degrading gene network of MM exhibited 110 positively correlated edges (62.86%) and 65 negatively correlated edges (37.14%) (Table 3 , Fig. 7 d), corresponding to a positive-to-negative edge ratio of 1.69. This indicated that synergistic interactions predominated between monosaccharide-degrading genes and bacterial OTUs in this species. Table 2 Network Topological Characteristics Cellulase genes network (RO) Cellulase genes network (MM) Nodes 114 145 Edges 124 145 Positive edges (%) 88(70.97) 89(61.38) Negative edges (%) 36(29.03) 56(38.62) Positive/Negative edges 2.44 1.59 Average degree 2.175 2 Diameter 6 4 Average path length 4.068 2.615 Density 0.019 0.014 Modularity 0.676 0.738 Table 3 Network Topological Characteristics Monosaccharide catabolic genes network (RO) Monosaccharide catabolic genes network (MM) Nodes 126 134 Edges 130 175 Positive edges (%) 59(45.38) 110(62.86) Negative edges (%) 71(54.62) 65(37.14) Positive/Negative edges 0.831 1.69 Average degree 2.063 2.612 Diameter 8 6 Average path length 4.092 3.274 Density 0.017 0.02 Modularity 0.702 0.521 Our functional prediction analysis indicated that genes associated with chitin digestion exhibited an overall higher predicted abundance in the gut microbiota of the omnivorous MM (Fig. 8 ). In particular, the predicted abundances of chitosanase and phospho-chitobiase in the gut microbiota of MM were significantly greater than those observed in the herbivorous RO (Figs. 8 b, c). Although the predicted abundances of chitinase and chitin disaccharide deacetylase did not differ significantly between the two species, MM nonetheless exhibited a consistently greater trend than RO did (Figs. 8 a, d). We conducted a Spearman correlation analysis between the 500 most abundant bacterial OTUs in the intestines of the two gerbil groups and chitin-degrading genes. In RO, 132 OTUs were significantly correlated with chitin-degrading genes (Table 4 ). Within their chitin-degradation gene network, 63 edges represented positive correlations (42.28%), and 86 represented negative correlations (57.72%), yielding a positive-to-negative correlation ratio of 0.73 (Table 4 , Fig. 9 a). This pattern suggests that antagonistic interactions predominated between chitin-degrading genes and bacterial OTUs in RO. In MM, 57 OTUs were significantly correlated with chitin-degrading genes (Table 4 ). Their gene network comprised 59 positively correlated edges (74.68%) and 20 negatively correlated edges (25.32%), with a positive-to-negative correlation ratio of 2.95 (Table 4 , Fig. 9 b), indicating that synergistic interactions predominated between chitin-degrading genes and bacterial OTUs in MM. Table 4 Network Topological Characteristics Chitinolytic genes network (RO) Chitinolytic genes network (MM) Nodes 132 57 Edges 149 79 Positive edges (%) 63(42.28) 59(74.68) Negative edges (%) 86(57.72) 20(25.32) Positive/Negative edges 0.733 2.95 Average degree 2.258 2.772 Diameter 6 4 Average path length 3.314 2.996 Density 0.017 0.049 Modularity 0.541 0.444 Discussion In this study, we used 16S rRNA sequencing to perform an ecological analysis of the gut microbiota of two sympatric desert rodent species, with a focus on the relationships between the gut microbial composition and function and their respective feeding strategies. Our results revealed that the two rodent species exhibited markedly distinct gut microbial community structures, a pattern consistent with previous research on wild rodents [34] . Moreover, we observed that diet strongly influences both the composition and functional potential of the gut microbiota, with significant differences in the predicted abundance of genes associated with specific metabolic functions among rodents fed different diets [35] . Building upon these findings, we conducted cross-species comparisons and discussions centred on two principal aspects: (i) the functional adaptations of the gut microbiota in herbivorous and omnivorous rodents for the digestion of complex carbohydrates and (ii) the potential link between the gut microbiota of omnivorous rodents and their capacity for chitin degradation. (i) Functional adaptations of the gut microbiota in herbivorous RO/omnivorous MM to complex carbohydrate digestion In this study, in comparison with the herbivorous RO, the omnivorous MM resulted in greater gut microbial diversity. Previous research has generally indicated that herbivorous mammals, owing to the higher fibre content and structural complexity of their diets, tend to possess richer gut microbial diversity [36] , which contrasts with the pattern observed here. In this study, however, the microbial diversity of the omnivorous MM was greater. We propose that this relatively elevated diversity may be closely associated with its varied diet. Omnivorous species consume a broader spectrum of food types, many of which contain complex components (such as chitin and various plant-derived and nonplant-derived polysaccharides) that are not readily digested by the host and therefore may require a more diverse microbial community for decomposition and metabolism. Such increased microbial diversity may, in turn, increase the ability of MM to exploit its diverse food resources more efficiently [37] . This study revealed that the gut of herbivorous RO not only contains a relatively high abundance of cellulose-degrading bacterial taxa— Muribaculaceae [31] —but also has the greatest predicted abundance of genes involved in the degradation of complex fibres, which is consistent with expectations based on its diet. Further correlation network analysis revealed that synergistic interactions predominated within the RO’s cellulose-degrading gene network (positive-to-negative edge ratio of 2.44), indicating substantial functional cooperation among its microbiota to support fibre breakdown. Unexpectedly, however, genes associated with monosaccharide digestion showed a higher predicted abundance in the omnivorous MM. We initially hypothesized that because cellulose must be fermented by microbes into volatile fatty acids (such as acetate and propionate) before being absorbed by the host and because monosaccharides are important intermediates in this process [38] , herbivores might also possess a high abundance of genes related to monosaccharide metabolism. In practice, the results revealed that the abundance of monosaccharide digestion genes was relatively low in RO, and its monosaccharide-degrading gene network was dominated by antagonistic interactions (positive-to-negative edge ratio of 0.83), suggesting that microbial competition or inhibition may influence this metabolic pathway. One possible explanation is that monosaccharides, as transient intermediates in the cellulose fermentation process, do not accumulate or persist in large quantities in the gut, thus reducing the need to maintain a high abundance of monosaccharide digestion genes [39] . In contrast, the monosaccharide-degrading gene network of MM displayed more pronounced synergistic interactions (positive-to-negative edge ratio of 1.69), indicating a more coordinated mechanism of monosaccharide utilization within its microbiota. Although primarily omnivorous, the MM gut also contains a notable abundance of cellulose-degrading genes and demonstrates a degree of synergistic interaction in the cellulose degradation network (positive-to-negative edge ratio of 1.59), which may facilitate the efficient breakdown of fibrous components when plant tissues or seed coats are consumed. From the perspective of digestive strategies—“yield maximization” versus “rate maximization”—the smaller-bodied, rate-maximizing MM [40] may enhance synergistic metabolic networks related to monosaccharide digestion within its microbiota to rapidly extract energy from easily degradable carbohydrates such as seed starch, thus adapting to its shorter food retention time and higher energy turnover demands. (ii) Omnivorous MM and Chitin Degradation The gut microbiome of the omnivorous MM differs markedly from that of RO, exhibiting greater species diversity. With respect to community structure, the gut of MM is enriched with multiple functionally specialized microbial groups. Previous studies have suggested that chitin intake may induce intestinal inflammation in mammals [41] , whereas the markedly enriched Lachnospiraceae_NK4A136_group in MM may contribute to the regulation of inflammation through the production of butyrate [42] . As the principal energy source for intestinal epithelial cells, butyrate can reduce intestinal lumen pH, inhibit pathogenic colonization, increase the expression of tight junction proteins, improve epithelial barrier integrity, and attenuate inflammatory responses [43] . Consequently, the enrichment of the Lachnospiraceae_NK4A136_group may represent an adaptive mechanism that mitigates chitin-induced inflammation and supports host health. Furthermore, the pronounced enrichment of Desulfovibrio in the gut of MM plays a pivotal role in chitin degradation. Chitin breakdown is often accompanied by the release of H₂; in anaerobic environments, the accumulation of H₂ can inhibit the feedback of chitin-fermenting enzymes, thus slowing further degradation. As a sulphate-reducing bacterium [44] , Desulfovibrio can utilize H₂ as an electron donor to reduce sulphate to hydrogen sulphide, effectively removing H₂ and alleviating its inhibitory effect on the degradation process and thus markedly enhancing the metabolic activity of chitin-degrading bacteria [45] . This interspecies hydrogen transfer exemplifies a typical interaction model for the efficient degradation of complex polymers in anaerobic systems, underscoring the importance of microbial functional specialization and cooperation in host digestive adaptation. In addition, the gut of MM is enriched in genera such as Eubacterium_siraeum_group , Lachnoclostridium , and GCA-900066575 , which may play important roles in energy metabolism, cell signal transduction, and immune regulation [46] , although their precise mechanisms warrant further investigation. Interestingly, although genes associated with chitin digestion were identified in the gut microbiomes of both gerbil species—likely reflecting the incidental ingestion of insects during their natural foraging behaviour [47] —the omnivorous MM displayed a markedly higher predicted abundance of chitin-degrading genes. These genes encode key genes that are integral to chitin breakdown, such as chitinase, chitosanases, phospho-chitobiase, and chitin disaccharide deacetylase [48] . This elevated abundance of degradation-related genes is likely to increase the metabolic capacity of MM for chitin, thus conferring a digestive adaptive advantage. Constraints, Limitations, and Scientific Validity This study primarily undertook a descriptive analysis of the gut microbiomes of two gerbil species. Our research samples were collected during a single season, which may not fully capture the potential influence of seasonal variations on the diet and microbiome composition of gerbils [49] . Furthermore, each group comprised only 10 samples, and this limited sample size may reduce the statistical power and constrain the comprehensive evaluation of within-population variability. Nevertheless, as the study was carried out under natural habitat conditions and high-throughput sequencing technology was used to analyse the microbiome, the preliminary findings remain valuable. Although 16S rRNA sequencing technology offers a reliable means of characterizing the structure of microbial communities, its resolution at the species level is limited, and potential biases may arise when closely related taxa are distinguished. Such limitations could hinder the detection of subtle microbial differences between the two gerbil species. Moreover, variations in the copy number of the 16S rRNA gene among different microorganisms mean that species abundance estimates derived from this approach may not accurately represent their actual proportions within the community, potentially influencing subsequent dietary interpretations [50] . With respect to functional inference, while PICRUSt2 serves as a valuable tool for predicting potential functional profiles, its accuracy is highly dependent on the breadth of reference genome coverage and the precision of 16S sequence annotation. Given that current databases are still largely dominated by human, laboratory animal, and common environmental microorganisms, the functional annotation of the gut microbiota in wild rodents such as gerbils remains incomplete, which may introduce bias into certain functional predictions. Furthermore, bacterial genomes exhibit considerable plasticity and strain-level heterogeneity, and 16S data alone cannot fully capture their true functional potential. Consequently, functional differences inferred from PICRUSt2 should be interpreted with caution [51] . Nonetheless, despite these limitations, the present study—conducted under natural conditions and employing widely validated methodological approaches—provides valuable baseline information for understanding the dietary adaptations and gut microbiota interactions of sympatric gerbils. 16S rRNA sequencing technology played a pivotal role in this study. Its strength lies in its ability to address multiple ecological questions from a single set of sequencing data, enabling efficient data integration and analysis. Although certain limitations exist in sequencing depth, the resulting data nonetheless substantially advanced our understanding of differences in gut microbiota composition between sympatrically coexisting species—RO and MM—as well as the functional potential associated with their diets. We recommend that future research incorporate more precise approaches, such as metagenomics and metatranscriptomics, to validate and further refine these conclusions. We also urge readers to carefully consider the inherent limitations of the methods employed and their potential impact on the interpretation of results when inferences are drawn from this study. Conclusion Through a comparative analysis of the gut microbiota of sympatric RO (herbivorous) and MM (omnivorous), this study demonstrated that dietary divergence-driven microecological adaptation strategies constitute a key mechanism facilitating interspecies coexistence. MM exhibits greater microbial diversity and harbours microbial taxa and functional genes associated with monosaccharide metabolism, chitin degradation, and gut health, whereas RO is markedly enriched in microbial taxa and genes linked to fibre degradation. Functional network analysis further revealed that MM primarily participates in synergistic interactions within monosaccharide and chitin degradation networks, whereas RO shows stronger cooperative associations within cellulose degradation networks. This differentiation in functional network architecture illustrates how the two species, which share the same habitat, optimize the utilization of distinct food resources through divergence in microbial metabolic functions, thus minimizing niche overlap and fostering sympatric coexistence. From the perspective of microbial functional interactions, this study provides a novel perspective for understanding the mechanisms underlying coexistence among sympatric species. Declarations Authors’ contributions SY and HPF designed and coordinated the study. NL examined the animals and collected samples. QXL conducted data summarization. DYCand NL analyzed the data and wrote the manuscript. All authors contributed to the article and approved the submitted version. Funding information This experiment was funded by 2022 Inner Mongolia Autonomous Region Youth Science and Technology Talent Development Plan (NJYT22044), Specialised Projects for Scientific Research in First-Class Disciplines (YLXKZX-NND-005, YLXKZX-NND-031), Inner Mongolia Natural Science Foundation (2023MS03025), the Major Science and Technology Project of Inner Mongolia Autonomous Region (2021ZD0006), The National Natural Science Foundation of China (32060256, 32060395), Grassland Ecological Protection and Restoration Treatment Subsidy (RK2200000355), Basic Scientific Research Business Expenses of Universities Directly Under Inner Mongolia Autonomous Region (BR220106, BR221307, and BR221037), Higher Education Reform . Date Availability statement The original data are available in the NCBI data repository : https://www.ncbi.nlm.nih.gov/sra/PRJNA1274967. The original data are stored in the NCBI data repository for private peer review : https://www.ncbi.nlm.nih.gov/sra/PRJNA1274967. 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Supplementary Files SupplementaryFile1DetailedMethodsforAmplificationLibraryPreparationandSequencing.docx Supplementary information Additional file :Sample amplification, library preparation, and sequencing. NSTI value of sample OTUs. Prediction_KO. SupplementaryFile2NSTINearestSequencedTaxonIndexScores.xls SupplementaryFile3predictionKO.xls Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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17:46:56","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":56930,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7587564/v1/4868f1c413b4866ac53e6348.png"},{"id":93520759,"identity":"d23c2150-2402-4f02-942c-6c47dd5aabff","added_by":"auto","created_at":"2025-10-14 17:46:56","extension":"xml","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":104719,"visible":true,"origin":"","legend":"","description":"","filename":"4d51a111a7c14385a70d91af3a62cf3e1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7587564/v1/d3851c07040d7fa18d7f901a.xml"},{"id":93520753,"identity":"805761df-13e8-464c-bbeb-7848c3b73a82","added_by":"auto","created_at":"2025-10-14 17:46:56","extension":"html","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":113643,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7587564/v1/08b1c0d9f12dd62e880cffcb.html"},{"id":93520723,"identity":"74d2ccb3-aae4-4bd1-bd2b-347ed4786261","added_by":"auto","created_at":"2025-10-14 17:46:55","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":35387,"visible":true,"origin":"","legend":"\u003cp\u003eSequencing Quality and OTU Analysis. a: Venn diagram illustrating a comparison of the OTU counts between the two gerbil species. The two coloured ellipses represent the respective species, and the numbers indicate OTUs unique to each species or shared by both; b: Rarefaction curve of gut microbiota sequences\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7587564/v1/e966495f947dc49cbb262a48.jpg"},{"id":93520726,"identity":"841b9086-d6fa-40a2-a61d-59ed9a5d85f1","added_by":"auto","created_at":"2025-10-14 17:46:55","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":36914,"visible":true,"origin":"","legend":"\u003cp\u003eVariations in gut microbiota alpha diversity across different host species. a: Chao diversity index; b: Sobs diversity index; P \u0026lt; 0.05\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7587564/v1/cc2936529f9703078f84603f.jpg"},{"id":93522090,"identity":"5cdb1bea-96e1-4782-af10-dc18a9b32187","added_by":"auto","created_at":"2025-10-14 18:10:55","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":37307,"visible":true,"origin":"","legend":"\u003cp\u003eβ diversity of the gut microbial communities of the two gerbil species. a: In the ANOSIM box plot, the X-axis denotes the grouping. The box labelled 'Between' indicates the distance values reflecting intergroup differences, whereas the other boxes indicate the distance values for intragroup differences. The Y-axis scale reflects the magnitude of these distance values. The R value typically ranges from 0 to 1; the closer the R value is to 1, the greater the intergroup differences relative to intragroup differences. b: NMDS analysis of the intestinal microbiota oftwo sympatrically coexisting gerbil species\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7587564/v1/4529677d9b291459af929598.jpg"},{"id":93520730,"identity":"589535ed-9837-4fb2-8cc6-f778dcda33cd","added_by":"auto","created_at":"2025-10-14 17:46:55","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":60103,"visible":true,"origin":"","legend":"\u003cp\u003eCircos plot illustrating the distribution of microbial species at the phylum level in the two gerbil groups. The left half of the circle depicts the species composition within the sample. The colour of the outer ribbon indicates the group of origin, whereasthe colour of the inner ribbon denotes the species identity. Ribbon length reflects the relative abundance of that species in the corresponding sample. The right half of the circle represents the proportional distribution of species at this taxonomic level across different samples.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7587564/v1/3df4db85b9df683208b153a1.jpg"},{"id":93521722,"identity":"ebec1284-5ad0-4ac6-bf70-2159a70d751e","added_by":"auto","created_at":"2025-10-14 18:02:55","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":75903,"visible":true,"origin":"","legend":"\u003cp\u003eLinear discriminant analysis (LDA) scores of the gut microbiota at the genus level in both RO and MM\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7587564/v1/1e02af4f332f11c21da16cd8.jpg"},{"id":93520734,"identity":"c76f1cdb-5225-4977-a9f1-94b6fbba4396","added_by":"auto","created_at":"2025-10-14 17:46:55","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":57781,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted abundance of genes related to carbohydrate metabolism. (a) Predicted abundance of genes involved in the degradation of complex fibres. (b) Stacked bar chart illustrating the relative contributions of the five most abundant KEGG categories associated with fibre digestion. (c) Predicted abundance of genes involved in the degradation of monosaccharides. (d) Stacked bar chart illustrating the relative contributions of the five most abundant KEGG categories associated with monosaccharide digestion. In Panels (a) and (c), each dot represents data from an individual rodent, while the lines and bars denote the means ± standard errors. Brackets indicate the results of pairwise Kruskal–Wallis tests. Adjusted P values are denoted as follows: (***) for P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7587564/v1/96dc822baab7e026afefb874.jpg"},{"id":93521726,"identity":"e8435217-54e3-4d04-89e9-d227c256edc6","added_by":"auto","created_at":"2025-10-14 18:02:55","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":125899,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction network maps illustrating the relationships between the top 500 most abundant bacterial OTUs of RO and MM and cellulose-degrading genes (a and b), as well as monosaccharide-degrading genes (c and d). Red lines denote positive associations, while green lines denote negative associations. Red nodes represent bacterial OTUs, whereas nodes in other colours correspond to distinct cellulose- and monosaccharide-degrading genes. The relative abundances of these genes are reflected by the sizes of the dots.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7587564/v1/3311a03fa000c7b77cbd0eaa.jpg"},{"id":93520744,"identity":"0004043c-5d22-4fa2-b4c8-cfb4fbb0e34d","added_by":"auto","created_at":"2025-10-14 17:46:55","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":56835,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted abundance of genes associated with chitin degradation. Dots represent data from individual rodents, while lines and bars indicate the means ± standard errors. Brackets denote the results of pairwise Kruskal‒Wallistests. Adjusted P values are represented by the following symbols: (*) indicates P \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7587564/v1/8a625475f25ba8ff15a331ec.jpg"},{"id":93521443,"identity":"609a2a3a-b065-4018-98b7-f0ad4a995aab","added_by":"auto","created_at":"2025-10-14 17:54:55","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":94099,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction network map depicting the top 500 most abundant bacterial OTUs and chitin-degrading genes in RO and MM. Red lines denote positive associations, whereas green lines denote negative associations. Red nodes correspond to bacterial OTUs, whereasnodes in other colours represent distinct chitin-degrading genes. The relative abundance of chitin-degrading genes is reflected by the size of the dots.\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7587564/v1/ec38aa03eb43bbe30536059e.jpg"},{"id":94768416,"identity":"81a025f9-7d73-4542-ab31-aafb91fb25b3","added_by":"auto","created_at":"2025-10-30 13:23:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1438834,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7587564/v1/2668a7cd-b7d3-4437-8ff9-b9bf93b5e180.pdf"},{"id":93522153,"identity":"93aa8223-9fe8-418b-afa4-672fe2baa297","added_by":"auto","created_at":"2025-10-14 18:18:55","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19866,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdditional file :Sample amplification, library preparation, and sequencing. NSTI value of sample OTUs. Prediction_KO.\u003c/p\u003e","description":"","filename":"SupplementaryFile1DetailedMethodsforAmplificationLibraryPreparationandSequencing.docx","url":"https://assets-eu.researchsquare.com/files/rs-7587564/v1/9ce4e4387c3e562f47230788.docx"},{"id":93520736,"identity":"8f24e5fd-4654-4b76-b5cb-20dab2b5b5dc","added_by":"auto","created_at":"2025-10-14 17:46:55","extension":"xls","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":123414,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile2NSTINearestSequencedTaxonIndexScores.xls","url":"https://assets-eu.researchsquare.com/files/rs-7587564/v1/f8f9e05f12cedbb1952ab914.xls"},{"id":93521449,"identity":"6fedcf53-b462-4517-807d-4a371e12291d","added_by":"auto","created_at":"2025-10-14 17:54:56","extension":"xls","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":2571264,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile3predictionKO.xls","url":"https://assets-eu.researchsquare.com/files/rs-7587564/v1/370b88e52d94a9c8d697086d.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"The gut microecology of the two sympatric gerbil species varies with diet","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe gut microbiome of mammals constitutes a highly complex and biodiverse ecosystem, with the total number of genes it encodes estimated to be approximately 150 times greater than that of the host\u0026rsquo;s own genome\u003csup\u003e[1]\u003c/sup\u003e, and is therefore often referred to as the host\u0026rsquo;s \u0026ldquo;second genome\u0026rdquo;. The structure and function of this microbial community are governed by a range of multidimensional factors, including intrinsic determinants such as the host\u0026rsquo;s genetic background, taxonomy, and developmental stage, as well as extrinsic influences such as dietary composition, geographic habitat, and social behaviour\u003csup\u003e[2]\u003c/sup\u003e. Among these numerous factors, host phylogenetic relationships and dietary strategies are widely recognized as two principal driving forces that shape the composition and functional evolution of the gut microbiota\u003csup\u003e[3]\u003c/sup\u003e. Phylogenetic relationships capture the conservative selection of microbial communities over the course of the host\u0026rsquo;s long-term evolutionary history\u003csup\u003e[4]\u003c/sup\u003e, whereas diet exerts direct selective pressures through the daily intake of nutrients, thus driving the dynamic adaptation and functional differentiation of the microbial community\u003csup\u003e[5]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe extensive metabolic diversity within gut microbial communities endows the host with a broad array of distinctive functions and directly contributes to its physiological activities and energy metabolism. Consequently, there is an increasing consensus that the microbiome may serve as a key mediator enabling the host to achieve rapid ecological adaptation and evolutionary diversification\u003csup\u003e[6]\u003c/sup\u003e. Among these functions, one of particular importance is the ability of microbes to degrade complex biopolymers that the host itself cannot process, thus expanding the range of nutrient resources available to the host. For instance, structural components of plant cell walls\u0026mdash;such as cellulose and hemicellulose\u0026mdash;are carbohydrate polymers composed of β-1,4-glycosidic bonds that cannot be hydrolysed by the host\u0026rsquo;s endogenous enzymatic systems\u003csup\u003e[7]\u003c/sup\u003e. Thus, most herbivorous mammals depend on gut microbes to perform anaerobic fermentation, transforming these otherwise indigestible polysaccharides into absorbable metabolites, such as short-chain fatty acids, which constitute important sources for maintaining the host\u0026rsquo;s energy balance. Another biopolymer of notable significance is chitin. As the second most abundant polysaccharide in nature\u003csup\u003e[8],\u003c/sup\u003e chitin is widely distributed in the exoskeletons of arthropods and the cell walls of fungi and is a linear polymer composed of N-acetylglucosamine units linked by β-1,4 bonds. It is a substantial dietary component for many insectivorous and omnivorous vertebrates. Research on mammals specialized in consuming ants and termites\u0026mdash;such as \u003cem\u003eMyrmecophaga tridactyla\u003c/em\u003e, \u003cem\u003eManis pentadactyla\u003c/em\u003e, and \u003cem\u003eOrycteropus afer\u003c/em\u003e\u0026mdash;has revealed that despite their considerable phylogenetic distance, these hosts have evolved strikingly convergent gut microbial communities\u003csup\u003e[9]\u003c/sup\u003e, suggesting that microbes may play a pivotal role in facilitating adaptation to chitin-rich diets. However, little is currently known about whether such microbial convergence is widespread among other insectivorous taxa, whether these communities indeed increase chitin digestion efficiency, and which specific microbial taxa are involved and play critical roles in this process\u003csup\u003e[10]\u003c/sup\u003e. In particular, for omnivorous species that are not yet specialized that regularly ingest chitin, whether their microbiomes undergo similar adaptive modifications remains to be systematically investigated.\u003c/p\u003e\u003cp\u003eRodents serve as excellent models for investigating fundamental questions in host\u0026ndash;microbe interactions. As the most species-rich order among mammals, they exhibit diverse ecological adaptations, occupy a broad range of habitats, and display notable dietary variation\u003csup\u003e[11]\u003c/sup\u003e. Consequently, rodents have become a widely utilized model system in comparative biology\u003csup\u003e[12]\u003c/sup\u003e, particularly within the realm of host\u0026ndash;microbe interactions\u003csup\u003e[13]\u003c/sup\u003e. Both RO and MM belong to the order Rodentia and the family Muridae and are commonly found in the desert regions of Central Asia. The former is the sole representative of the genus Rhombomys, whereas the latter is classified under the genus Meriones. Both species are widely distributed across northern China\u003csup\u003e[14]\u003c/sup\u003e. Notably, in certain habitats, their ranges frequently overlap. In regions where RO is present, MM often occurs as a sympatric species, resulting in a stable pattern of coexistence\u003csup\u003e[15]\u003c/sup\u003e. Research has indicated that this coexistence is largely facilitated by pronounced trophic niche differentiation: RO is a specialized herbivore with a restricted diet, feeding primarily on moisture-rich green stems and leaves of plants; in contrast, MM is a typical omnivore, consuming plant matter alongside a substantial proportion of insects and seeds\u003csup\u003e[16]\u003c/sup\u003e. This marked dietary divergence mitigates direct competition for limited resources and constitutes a key niche differentiation strategy that underpins their long-term stable coexistence within overlapping distribution areas.\u003c/p\u003e\u003cp\u003eTo determine whether the aforementioned dietary divergence is reflected in the gut microbiome structure, which is closely associated with digestive function, this study employed 16S rRNA gene high-throughput sequencing to characterize the gut microbial communities of two sympatric gerbil species inhabiting the same region but adopting distinct feeding strategies. Predictive metagenomics (PICRUSt2) was further applied to infer their potential functional capacities. On the basis of the documented dietary differences between the two species, we formulated the following hypotheses. (i) The two gerbil species will display markedly distinct gut microbiome structures; (ii) The microbial communities, in terms of both taxonomic composition and predicted functions, will be enriched with microbial taxa and functional gene sets associated with the decomposition and metabolism of the principal components of their natural diets (e.g., plant cellulose for RO and chitin for MM).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eRodent collection\u003c/h2\u003e\u003cp\u003eIn 2024, two rodent species were collected from the desert region in the southern part of Alxa Left Banner, Alxa League, Inner Mongolia Autonomous Region, China (37.887497\u0026deg;N, 105.381715\u0026deg;E). Within the sampling area, live traps baited with fresh peanuts and carrots were employed to capture the rodents. The traps were set in the early morning and inspected in the evening, as well as the following morning. Previous research has indicated that short-term bait consumption in live traps does not significantly influence the gut microbiome of rodents\u003csup\u003e[17]\u003c/sup\u003e. A total of 20 rodents were captured, comprising 10 RO and 10 MM rodents (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe collected cecal content samples from live-captured rodents. All samples were obtained under sterile conditions, placed in 2 mL sterile cryotubes, promptly frozen in liquid nitrogen, and subsequently stored at -80\u0026deg;C in an ultra-low temperature environment. We euthanized RO and MM using inhalation overdose of isoflurane, with 3.5% isoflurane used for induction. Euthanasia of RO and MM was performed by cervical dislocation after confirmation of unconsciousness. For small rodents weighing less than 300 g, cervical dislocation was carried out by experienced personnel. This method was chosen due to its efficiency and minimal distress to the animal. Our study was approved by the Ethics Committee of Inner Mongolia Agricultural University under the approval number NND2017012.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDNA Extraction\u003c/h3\u003e\n\u003cp\u003eTotal DNA was extracted from the caecal contents using the FastDNA\u0026reg; Spin Kit for Soil, in accordance with the manufacturer\u0026rsquo;s instructions. Caecal contents were selected because the caecum serves as the primary site of food fermentation in rodents and is characterized by high microbial density and activity\u003csup\u003e[18]\u003c/sup\u003e. The extracted DNA was stored on dry ice and subsequently transported to Shanghai Majorbio Bio-Pharm Technology Co., Ltd. The upstream primer 338F (ACTCCTACGGGAGGCAGCAG) and downstream primer 806R (GGACTACHVGGGTWTCTAAT) were used to amplify the V3\u0026ndash;V4 region of the bacterial 16S rRNA gene via PCR\u003csup\u003e[19]\u003c/sup\u003e, after which the resulting amplicons were pooled and purified. Library preparation was conducted using the NEXTFLEX Rapid DNA-Seq Kit, and sequencing was performed to generate 2 \u0026times; 300 base-pair paired-end reads. Detailed descriptions of the amplification, library preparation, and sequencing procedures are provided in the Supplementary Materials (Supplementary File 1). Sequence reads have been deposited in the NCBI database under accession number (PRJNA1274967).\u003c/p\u003e\u003cp\u003eQuality control of the raw sequencing data was conducted using fastp (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/OpenGene/fastp\u003c/span\u003e\u003cspan address=\"https://github.com/OpenGene/fastp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, version 0.20.0)\u003csup\u003e[20]\u003c/sup\u003e, whereas FLASH (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cbcb.umd.edu/software/flash\u003c/span\u003e\u003cspan address=\"http://www.cbcb.umd.edu/software/flash\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, version 1.2.7) was employed for read merging\u003csup\u003e[21]\u003c/sup\u003e. Bases with quality scores below 20 at the ends of the reads were removed. A 50 bp sliding window was applied, and if the average quality within the window fell below 20, bases from the start of the window to the 3\u0026prime; end were trimmed. Reads shorter than 50 bp after quality control, as well as those containing N bases, were discarded. Paired-end reads were merged into single sequences on the basis of overlapping regions, requiring a minimum overlap of 10 bp. A maximum mismatch rate of 0.2 was permitted within the overlap region, and sequences not meeting these criteria were excluded. Samples were identified according to barcodes and primers at both ends of the sequences, with read orientations adjusted accordingly. Zero mismatches were allowed for the barcodes, and up to two mismatches were permitted for the primers. OTU clustering at 97% sequence similarity was performed using UPARSE (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://drive5.com/uparse/\u003c/span\u003e\u003cspan address=\"http://drive5.com/uparse/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, version 7.1). Reads identified as originating from archaea, chloroplasts, or mitochondria were removed from further analysis\u003csup\u003e[22]\u003c/sup\u003e. Taxonomic classification of each sequence was carried out using the RDP classifier (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rdp.cme.msu.edu/\u003c/span\u003e\u003cspan address=\"http://rdp.cme.msu.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, version 2.2) against the Silva 16S rRNA database (v138), with a confidence threshold of 70%\u003csup\u003e[23]\u003c/sup\u003e.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eWe used QIIME2 software to generate microbial abundance tables across various taxonomic levels, clustered bacterial OTUs using Uparse software with a 97% sequence similarity threshold, and performed statistical analyses of OTUs using Usearch software to determine the composition and quantity of shared and unique OTUs in the gut microbiota of Rhombomys opimus and \u003cem\u003eMeriones unguiculatus\u003c/em\u003e. To compare gut microbial community diversity between the two gerbil species, we first calculated alpha diversity indices of the gut microbial communities using Mothur software and then applied Kruskal\u0026ndash;Wallis tests to assess differences in alpha diversity, followed by post hoc tests to further identify significant intergroup differences. To subsequently examine differences in the gut microbial community composition, we computed pairwise community dissimilarities for all the samples on the basis of the Bray\u0026ndash;Curtis distance\u003csup\u003e[24]\u003c/sup\u003e, unweighted UniFrac distance, and weighted UniFrac distance\u003csup\u003e[25]\u003c/sup\u003e and compared the compositional characteristics of the gut microbiota between the two species. Concurrently, nonmetric multidimensional scaling (NMDS) was employed to construct a visual representation, providing an intuitive depiction of similarities and differences among gut microbial communities from different samples. To further validate the microbiome changes (including community dispersion and compositional differences), we adopted two analytical approaches: (1) the PERMDISP test (with 999 permutations) in QIIME2 to evaluate the homogeneity of samples within groups in relation to community dispersion and (2) the calculation of Bray\u0026ndash;Curtis distances between paired samples within the same species (intraspecific), followed by Kruskal\u0026ndash;Wallis tests with post hoc multiple comparisons to assess the extent of intraspecific community variation.\u003c/p\u003e\u003cp\u003eTo assess the composition of the bacterial communities in the two gerbil groups, we employed the taxa-barplot tool in QIIME2 to visualize taxonomic abundance. To evaluate differences in bacterial communities between the groups, we used the LEfSe function in STAMP software to identify bacterial taxa exhibiting significantly different abundances at the genus level. Prior to conducting the differential analysis, we excluded bacterial taxa whose read counts were less than 20 to eliminate rare taxa\u003csup\u003e[26]\u003c/sup\u003e, and the analysis was primarily performed using default parameters.\u003c/p\u003e\u003cp\u003eWe employed the PICRUSt2 plugin in QIIME 2 to generate predicted metagenomic functional profiles on the basis of 16S rRNA sequencing data\u003csup\u003e[27]\u003c/sup\u003e. In this process, all OTUs with a nearest sequenced taxon index (NSTI) value exceeding 2.0 were excluded. By acknowledging the potential limitations inherent in predictive metagenomic analysis, we prioritized pre‑established hypotheses and concentrated on assessing the predicted abundance of functional categories associated with biopolymer degradation.\u003c/p\u003e\u003cp\u003eWe linked the glycoside hydrolase (GH) subset from the Carbohydrate-Active enZymes (CAZy) database with the KEGG Orthology (KO) database\u003csup\u003e[28]\u003c/sup\u003e to identify metabolic pathways relevant to our functional predictions. We examined genes involved in the digestion of simple sugars, including α-glucosidase (K01187), oligosaccharide-1,6-glucosidase (K01182), α-amylase (K07405), maltose-6\u0026rsquo;-phosphate glucosidase (K01232), and α-amylase (K01176). To assess the microbial capacity for metabolizing complex fibres, we also analysed the predicted abundances of genes associated with complex fibre degradation, such as β-glucosidase (K05349), endoglucanase (K01179), endo-1,4-β-xylanase (K01181), β-glucosidase (K05350), and xylan-1,4-β-xylosidase (K01198), by KEGG Orthology. Furthermore, we compared the relative abundances of multiple KEGG Orthology genes linked to chitin degradation\u003csup\u003e[29]\u003c/sup\u003e, including chitinase (K01183), chitosanase (K01233), phospho-chitobiase (K01222), and chitin disaccharide deacetylase (K03478). The predicted abundances of these genes in the two gerbil groups were compared using the Kruskal\u0026ndash;Wallis test, followed by post hoc analysis.\u003c/p\u003e\u003cp\u003eAn interaction network map of bacterial OTUs (top 500 in abundance) from RO and MM associated with cellulose, monosaccharide, and chitin degradation genes was generated using the \u0026ldquo;psych\u0026rdquo; package in R v4.5.1 and Gephi v0.9.2\u003csup\u003e[30]\u003c/sup\u003e. The network topology features were subsequently calculated with Gephi v0.9.2.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of the study species\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTaxonomicfamily\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSamplesize (n) ♂: ♀\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAverage weight (g)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDiet\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRhombomys opimus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMuridae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 : 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e164.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePlant foliage\u003csup\u003e[16]\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMeriones meridianus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMuridae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 : 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e62.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePlant foliage, insects, seeds\u003csup\u003e[16]\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e(Note: Based on the dietary composition of the two gerbil species, we categorized them as herbivorous (RO), and omnivorous (MM), the same below).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThis study examined the microbial composition of the caecal contents of two sympatric gerbil species. A total of 20 samples were analysed, with the number of valid sequences per sample ranging from 29,592 to 36,472 and an average of 33,770 valid sequences. At the 97% sequence similarity threshold, 5,562 core OTUs were identified. MM had 4,073 shared OTUs and 2,715 unique OTUs, whereas RO had 2,747 shared OTUs and 1,489 unique OTUs (Fig.\u0026nbsp;1a). These findings suggest that the intestinal tracts of the two species contain a substantial proportion of shared bacterial taxa, along with distinct microbial communities unique to each species. Rarefaction curves indicated that the estimates of species richness approached a plateau and were unbiased, implying that the current sequencing depth adequately captured the vast majority of the bacterial diversity present in the samples, thus supporting its suitability for subsequent data analyses (Fig.\u0026nbsp;1b).\u003c/p\u003e\u003cp\u003eWe assessed the alpha diversity indices of the gut microbial communities of two sympatric gerbil species using the Chao1 and Sobs indices to evaluate differences in species richness. The results indicated that the omnivorous MM resulted in higher gut microbial alpha diversity, whereas the herbivorous RO resulted in lower alpha diversity. Nevertheless, statistical analyses revealed that these differences were not significant (Chao1 index: P\u0026thinsp;=\u0026thinsp;0.1595; Sobs index: P\u0026thinsp;=\u0026thinsp;0.2484) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, b).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIndividual rodents displayed distinct microbiota compositions. ANOSIM analysis was employed to assess intra- and intergroup differences between the two gerbil species, revealing that the omnivorous MM exhibited greater interindividual microbiota variation than the herbivorous RO did. Furthermore, interspecific differences were markedly greater than intraspecific differences (R\u0026thinsp;=\u0026thinsp;0.6089, P\u0026thinsp;=\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). These findings were corroborated by NMDS analysis, which clearly revealed species-level clustering of the microbiota in the two gerbil species (stress\u0026thinsp;=\u0026thinsp;0.08, P\u0026thinsp;=\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). PERMANOVA analysis further confirmed that host species significantly influenced microbiome structure (F\u0026thinsp;=\u0026thinsp;18.25, P\u0026thinsp;=\u0026thinsp;0.001). Collectively, these results indicate that the gut microbiota structures of the two gerbil species differ substantially.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe examined the bacterial composition of the gut microbiota of the two gerbil species at the phylum level and identified distinct abundance patterns. In MM, the gut microbiota was predominantly composed of Firmicutes (RO: 35%; MM: 65%), whereas in RO, Bacteroidota prevailed (RO: 63%; MM: 37%). Desulfobacterota and Campilobacterota were markedly enriched in the gut of the omnivorous MM. In contrast, Patescibacteria, Verrucomicrobiota, Actinobacteriota, Proteobacteria, Cyanobacteria, and Spirochaetota were more abundant in the gut of the herbivorous RO (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAt the genus level, we conducted a LefSe analysis (LDA\u0026thinsp;\u0026gt;\u0026thinsp;3) on the gut microbiota of the two gerbil species. The results indicated that each species possessed its own uniquely enriched genera. In RO, the significantly enriched genera included \u003cem\u003enorank_f__Muribaculaceae\u003c/em\u003e (specializing in the degradation of complex polysaccharides such as plant fibres)\u003csup\u003e[31]\u003c/sup\u003e, \u003cem\u003eWeissella\u003c/em\u003e, \u003cem\u003eMonoglobus\u003c/em\u003e, \u003cem\u003eCandidatus_Saccharimonas\u003c/em\u003e, \u003cem\u003enorank_o__Clostridia_UCG-014\u003c/em\u003e, \u003cem\u003eEnterococcus\u003c/em\u003e, \u003cem\u003enorank_o__RF39\u003c/em\u003e, \u003cem\u003eAlcanivorax\u003c/em\u003e, and \u003cem\u003eDubosiella\u003c/em\u003e. In contrast, the gut microbiota of MM was significantly enriched in \u003cem\u003eLactobacillus\u003c/em\u003e (a genus of bacteria capable of lactic acid fermentation, converting carbohydrates into lactic acid)\u003csup\u003e[32]\u003c/sup\u003e, \u003cem\u003eLachnospiraceae_NK4A136_group\u003c/em\u003e, \u003cem\u003eDesulfovibrio\u003c/em\u003e (a sulphate-reducing bacterium and efficient hydrogen scavenger)\u003csup\u003e[33]\u003c/sup\u003e, \u003cem\u003enorank_f__Lachnospiraceae\u003c/em\u003e, \u003cem\u003eEubacterium_siraeum_group\u003c/em\u003e, \u003cem\u003eHelicobacter\u003c/em\u003e, \u003cem\u003eLachnospiraceae_UCG-006\u003c/em\u003e, \u003cem\u003eColidextribacter\u003c/em\u003e, \u003cem\u003eGCA-900066575\u003c/em\u003e, and \u003cem\u003eLachnoclostridium\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWhen the microbial OTUs identified in our study were compared with the reference genomes in the PICRUSt2 database, the nearest sequenced taxon index (NSTI) for all 5,562 OTUs was below the threshold of 2.0; consequently, none were excluded from the subsequent PICRUSt2 functional prediction analysis (Supplementary File 2). The NSTI values across all the OTUs were generally low (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation: 0.321\u0026thinsp;\u0026plusmn;\u0026thinsp;0.194; median: 0.279). A significant difference in the weighted mean NSTI values was observed between the two gerbil species (Kruskal\u0026ndash;Wallis test: H\u0026thinsp;=\u0026thinsp;22.182, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The NSTI values obtained in this study are consistent with those reported in human microbiome research, suggesting that metagenomic functional predictions based on 16S rRNA gene sequencing data are reliable.\u003c/p\u003e\u003cp\u003eFunctional prediction analysis revealed that the predicted abundance of fibre degradation-related genes in the microbiota of the herbivorous RO was markedly greater, whereas it was substantially lower in the omnivorous MM (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, b; Kruskal\u0026ndash;Wallis test: H\u0026thinsp;=\u0026thinsp;14.29, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, the predicted abundance of genes associated with monosaccharide digestion was greater in the omnivorous MM and lower in the herbivorous RO (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ec, d; H\u0026thinsp;=\u0026thinsp;14.29, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In both gerbil species, the most prevalent gene linked to complex fibre metabolism was β-glucosidase (K05349) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eb), whereas the most prevalent gene associated with monosaccharide digestion was α-glucosidase (K01187) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ed) (Supplementary File 3).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn this study, Spearman correlation analyses were performed between the 500 most abundant bacterial OTUs in the intestinal tracts of the two gerbil species and genes associated with cellulose and monosaccharide degradation, respectively.. In RO, 114 OTUs were significantly correlated with cellulose-degrading genes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), whereas 126 OTUs were significantly correlated with monosaccharide-degrading genes (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Within the cellulose-degrading gene network of RO, 88 edges represented positive correlations (70.97%), and 36 represented negative correlations (29.03%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003ea), yielding a positive-to-negative edge ratio of 2.44 and indicating a predominance of synergistic interactions. In the MM cellulose-degrading gene network, 89 positively correlated edges (61.38%) and 56 negatively correlated edges (38.62%) were observed (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eb), resulting in a positive-to-negative edge ratio of 1.59, likewise indicating a predominance of synergistic interactions, although the degree of synergy was lower than that observed in RO. In the monosaccharide-degrading gene network of RO, 59 positively correlated edges (45.38%) and 71 negatively correlated edges (54.62%) were identified (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003ec), resulting in a positive-to-negative edge ratio of 0.831, suggesting that antagonistic interactions predominated between monosaccharide-degrading genes and bacterial OTUs. Conversely, the monosaccharide-degrading gene network of MM exhibited 110 positively correlated edges (62.86%) and 65 negatively correlated edges (37.14%) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003ed), corresponding to a positive-to-negative edge ratio of 1.69. This indicated that synergistic interactions predominated between monosaccharide-degrading genes and bacterial OTUs in this species.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNetwork Topological Characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCellulase genes network (RO)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCellulase genes network (MM)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNodes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e145\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEdges\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e145\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive edges (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e88(70.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89(61.38)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative edges (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36(29.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56(38.62)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive/Negative edges\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiameter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage path length\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.615\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDensity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModularity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.676\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.738\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNetwork Topological Characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMonosaccharide catabolic genes network (RO)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMonosaccharide catabolic genes network (MM)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNodes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e134\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEdges\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e175\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive edges (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59(45.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e110(62.86)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative edges (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71(54.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65(37.14)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive/Negative edges\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.831\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.612\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiameter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage path length\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.274\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDensity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModularity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.702\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.521\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eOur functional prediction analysis indicated that genes associated with chitin digestion exhibited an overall higher predicted abundance in the gut microbiota of the omnivorous MM (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e). In particular, the predicted abundances of chitosanase and phospho-chitobiase in the gut microbiota of MM were significantly greater than those observed in the herbivorous RO (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eb, c). Although the predicted abundances of chitinase and chitin disaccharide deacetylase did not differ significantly between the two species, MM nonetheless exhibited a consistently greater trend than RO did (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003ea, d).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe conducted a Spearman correlation analysis between the 500 most abundant bacterial OTUs in the intestines of the two gerbil groups and chitin-degrading genes. In RO, 132 OTUs were significantly correlated with chitin-degrading genes (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Within their chitin-degradation gene network, 63 edges represented positive correlations (42.28%), and 86 represented negative correlations (57.72%), yielding a positive-to-negative correlation ratio of 0.73 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003ea). This pattern suggests that antagonistic interactions predominated between chitin-degrading genes and bacterial OTUs in RO. In MM, 57 OTUs were significantly correlated with chitin-degrading genes (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Their gene network comprised 59 positively correlated edges (74.68%) and 20 negatively correlated edges (25.32%), with a positive-to-negative correlation ratio of 2.95 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003eb), indicating that synergistic interactions predominated between chitin-degrading genes and bacterial OTUs in MM.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNetwork Topological Characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChitinolytic genes network (RO)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChitinolytic genes network (MM)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNodes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEdges\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive edges (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63(42.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59(74.68)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative edges (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e86(57.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20(25.32)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive/Negative edges\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.772\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiameter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage path length\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.996\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDensity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModularity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.444\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we used 16S rRNA sequencing to perform an ecological analysis of the gut microbiota of two sympatric desert rodent species, with a focus on the relationships between the gut microbial composition and function and their respective feeding strategies. Our results revealed that the two rodent species exhibited markedly distinct gut microbial community structures, a pattern consistent with previous research on wild rodents\u003csup\u003e[34]\u003c/sup\u003e. Moreover, we observed that diet strongly influences both the composition and functional potential of the gut microbiota, with significant differences in the predicted abundance of genes associated with specific metabolic functions among rodents fed different diets\u003csup\u003e[35]\u003c/sup\u003e. Building upon these findings, we conducted cross-species comparisons and discussions centred on two principal aspects: (i) the functional adaptations of the gut microbiota in herbivorous and omnivorous rodents for the digestion of complex carbohydrates and (ii) the potential link between the gut microbiota of omnivorous rodents and their capacity for chitin degradation.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e(i) Functional adaptations of the gut microbiota in herbivorous RO/omnivorous MM to complex carbohydrate digestion\u003c/h2\u003e\u003cp\u003eIn this study, in comparison with the herbivorous RO, the omnivorous MM resulted in greater gut microbial diversity. Previous research has generally indicated that herbivorous mammals, owing to the higher fibre content and structural complexity of their diets, tend to possess richer gut microbial diversity\u003csup\u003e[36]\u003c/sup\u003e, which contrasts with the pattern observed here. In this study, however, the microbial diversity of the omnivorous MM was greater. We propose that this relatively elevated diversity may be closely associated with its varied diet. Omnivorous species consume a broader spectrum of food types, many of which contain complex components (such as chitin and various plant-derived and nonplant-derived polysaccharides) that are not readily digested by the host and therefore may require a more diverse microbial community for decomposition and metabolism. Such increased microbial diversity may, in turn, increase the ability of MM to exploit its diverse food resources more efficiently\u003csup\u003e[37]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThis study revealed that the gut of herbivorous RO not only contains a relatively high abundance of cellulose-degrading bacterial taxa\u0026mdash;\u003cem\u003eMuribaculaceae\u003c/em\u003e\u003csup\u003e[31]\u003c/sup\u003e\u0026mdash;but also has the greatest predicted abundance of genes involved in the degradation of complex fibres, which is consistent with expectations based on its diet. Further correlation network analysis revealed that synergistic interactions predominated within the RO\u0026rsquo;s cellulose-degrading gene network (positive-to-negative edge ratio of 2.44), indicating substantial functional cooperation among its microbiota to support fibre breakdown. Unexpectedly, however, genes associated with monosaccharide digestion showed a higher predicted abundance in the omnivorous MM. We initially hypothesized that because cellulose must be fermented by microbes into volatile fatty acids (such as acetate and propionate) before being absorbed by the host and because monosaccharides are important intermediates in this process\u003csup\u003e[38]\u003c/sup\u003e, herbivores might also possess a high abundance of genes related to monosaccharide metabolism. In practice, the results revealed that the abundance of monosaccharide digestion genes was relatively low in RO, and its monosaccharide-degrading gene network was dominated by antagonistic interactions (positive-to-negative edge ratio of 0.83), suggesting that microbial competition or inhibition may influence this metabolic pathway. One possible explanation is that monosaccharides, as transient intermediates in the cellulose fermentation process, do not accumulate or persist in large quantities in the gut, thus reducing the need to maintain a high abundance of monosaccharide digestion genes\u003csup\u003e[39]\u003c/sup\u003e. In contrast, the monosaccharide-degrading gene network of MM displayed more pronounced synergistic interactions (positive-to-negative edge ratio of 1.69), indicating a more coordinated mechanism of monosaccharide utilization within its microbiota. Although primarily omnivorous, the MM gut also contains a notable abundance of cellulose-degrading genes and demonstrates a degree of synergistic interaction in the cellulose degradation network (positive-to-negative edge ratio of 1.59), which may facilitate the efficient breakdown of fibrous components when plant tissues or seed coats are consumed. From the perspective of digestive strategies\u0026mdash;\u0026ldquo;yield maximization\u0026rdquo; versus \u0026ldquo;rate maximization\u0026rdquo;\u0026mdash;the smaller-bodied, rate-maximizing MM\u003csup\u003e[40]\u003c/sup\u003e may enhance synergistic metabolic networks related to monosaccharide digestion within its microbiota to rapidly extract energy from easily degradable carbohydrates such as seed starch, thus adapting to its shorter food retention time and higher energy turnover demands.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003e(ii) Omnivorous MM and Chitin Degradation\u003c/h3\u003e\n\u003cp\u003eThe gut microbiome of the omnivorous MM differs markedly from that of RO, exhibiting greater species diversity. With respect to community structure, the gut of MM is enriched with multiple functionally specialized microbial groups. Previous studies have suggested that chitin intake may induce intestinal inflammation in mammals\u003csup\u003e[41]\u003c/sup\u003e, whereas the markedly enriched \u003cem\u003eLachnospiraceae_NK4A136_group\u003c/em\u003e in MM may contribute to the regulation of inflammation through the production of butyrate\u003csup\u003e[42]\u003c/sup\u003e. As the principal energy source for intestinal epithelial cells, butyrate can reduce intestinal lumen pH, inhibit pathogenic colonization, increase the expression of tight junction proteins, improve epithelial barrier integrity, and attenuate inflammatory responses\u003csup\u003e[43]\u003c/sup\u003e. Consequently, the enrichment of the \u003cem\u003eLachnospiraceae_NK4A136_group\u003c/em\u003e may represent an adaptive mechanism that mitigates chitin-induced inflammation and supports host health. Furthermore, the pronounced enrichment of \u003cem\u003eDesulfovibrio\u003c/em\u003e in the gut of MM plays a pivotal role in chitin degradation. Chitin breakdown is often accompanied by the release of H₂; in anaerobic environments, the accumulation of H₂ can inhibit the feedback of chitin-fermenting enzymes, thus slowing further degradation. As a sulphate-reducing bacterium\u003csup\u003e[44]\u003c/sup\u003e, \u003cem\u003eDesulfovibrio\u003c/em\u003e can utilize H₂ as an electron donor to reduce sulphate to hydrogen sulphide, effectively removing H₂ and alleviating its inhibitory effect on the degradation process and thus markedly enhancing the metabolic activity of chitin-degrading bacteria\u003csup\u003e[45]\u003c/sup\u003e. This interspecies hydrogen transfer exemplifies a typical interaction model for the efficient degradation of complex polymers in anaerobic systems, underscoring the importance of microbial functional specialization and cooperation in host digestive adaptation. In addition, the gut of MM is enriched in genera such as \u003cem\u003eEubacterium_siraeum_group\u003c/em\u003e, \u003cem\u003eLachnoclostridium\u003c/em\u003e, and \u003cem\u003eGCA-900066575\u003c/em\u003e, which may play important roles in energy metabolism, cell signal transduction, and immune regulation\u003csup\u003e[46]\u003c/sup\u003e, although their precise mechanisms warrant further investigation.\u003c/p\u003e\u003cp\u003eInterestingly, although genes associated with chitin digestion were identified in the gut microbiomes of both gerbil species\u0026mdash;likely reflecting the incidental ingestion of insects during their natural foraging behaviour\u003csup\u003e[47]\u003c/sup\u003e\u0026mdash;the omnivorous MM displayed a markedly higher predicted abundance of chitin-degrading genes. These genes encode key genes that are integral to chitin breakdown, such as chitinase, chitosanases, phospho-chitobiase, and chitin disaccharide deacetylase\u003csup\u003e[48]\u003c/sup\u003e. This elevated abundance of degradation-related genes is likely to increase the metabolic capacity of MM for chitin, thus conferring a digestive adaptive advantage.\u003c/p\u003e\n\u003ch3\u003eConstraints, Limitations, and Scientific Validity\u003c/h3\u003e\n\u003cp\u003eThis study primarily undertook a descriptive analysis of the gut microbiomes of two gerbil species. Our research samples were collected during a single season, which may not fully capture the potential influence of seasonal variations on the diet and microbiome composition of gerbils\u003csup\u003e[49]\u003c/sup\u003e. Furthermore, each group comprised only 10 samples, and this limited sample size may reduce the statistical power and constrain the comprehensive evaluation of within-population variability. Nevertheless, as the study was carried out under natural habitat conditions and high-throughput sequencing technology was used to analyse the microbiome, the preliminary findings remain valuable.\u003c/p\u003e\u003cp\u003eAlthough 16S rRNA sequencing technology offers a reliable means of characterizing the structure of microbial communities, its resolution at the species level is limited, and potential biases may arise when closely related taxa are distinguished. Such limitations could hinder the detection of subtle microbial differences between the two gerbil species. Moreover, variations in the copy number of the 16S rRNA gene among different microorganisms mean that species abundance estimates derived from this approach may not accurately represent their actual proportions within the community, potentially influencing subsequent dietary interpretations\u003csup\u003e[50]\u003c/sup\u003e. With respect to functional inference, while PICRUSt2 serves as a valuable tool for predicting potential functional profiles, its accuracy is highly dependent on the breadth of reference genome coverage and the precision of 16S sequence annotation. Given that current databases are still largely dominated by human, laboratory animal, and common environmental microorganisms, the functional annotation of the gut microbiota in wild rodents such as gerbils remains incomplete, which may introduce bias into certain functional predictions. Furthermore, bacterial genomes exhibit considerable plasticity and strain-level heterogeneity, and 16S data alone cannot fully capture their true functional potential. Consequently, functional differences inferred from PICRUSt2 should be interpreted with caution\u003csup\u003e[51]\u003c/sup\u003e. Nonetheless, despite these limitations, the present study\u0026mdash;conducted under natural conditions and employing widely validated methodological approaches\u0026mdash;provides valuable baseline information for understanding the dietary adaptations and gut microbiota interactions of sympatric gerbils.\u003c/p\u003e\u003cp\u003e16S rRNA sequencing technology played a pivotal role in this study. Its strength lies in its ability to address multiple ecological questions from a single set of sequencing data, enabling efficient data integration and analysis. Although certain limitations exist in sequencing depth, the resulting data nonetheless substantially advanced our understanding of differences in gut microbiota composition between sympatrically coexisting species\u0026mdash;RO and MM\u0026mdash;as well as the functional potential associated with their diets. We recommend that future research incorporate more precise approaches, such as metagenomics and metatranscriptomics, to validate and further refine these conclusions. We also urge readers to carefully consider the inherent limitations of the methods employed and their potential impact on the interpretation of results when inferences are drawn from this study.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThrough a comparative analysis of the gut microbiota of sympatric RO (herbivorous) and MM (omnivorous), this study demonstrated that dietary divergence-driven microecological adaptation strategies constitute a key mechanism facilitating interspecies coexistence. MM exhibits greater microbial diversity and harbours microbial taxa and functional genes associated with monosaccharide metabolism, chitin degradation, and gut health, whereas RO is markedly enriched in microbial taxa and genes linked to fibre degradation. Functional network analysis further revealed that MM primarily participates in synergistic interactions within monosaccharide and chitin degradation networks, whereas RO shows stronger cooperative associations within cellulose degradation networks. This differentiation in functional network architecture illustrates how the two species, which share the same habitat, optimize the utilization of distinct food resources through divergence in microbial metabolic functions, thus minimizing niche overlap and fostering sympatric coexistence. From the perspective of microbial functional interactions, this study provides a novel perspective for understanding the mechanisms underlying coexistence among sympatric species.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSY and HPF designed and coordinated the study. NL examined the animals and collected samples. QXL conducted data summarization. DYCand NL analyzed the data and wrote the manuscript. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis experiment was funded by 2022 Inner Mongolia Autonomous Region Youth Science and Technology Talent Development Plan (NJYT22044), Specialised Projects for Scientific Research in First-Class Disciplines (YLXKZX-NND-005, YLXKZX-NND-031), Inner Mongolia Natural Science Foundation (2023MS03025), the Major Science and Technology Project of Inner Mongolia Autonomous Region (2021ZD0006), The National Natural Science Foundation of China (32060256, 32060395), Grassland Ecological Protection and Restoration Treatment Subsidy (RK2200000355), Basic Scientific Research Business Expenses of Universities Directly Under Inner Mongolia Autonomous Region (BR220106, BR221307, and BR221037), Higher Education Reform .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDate Availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original data are available in the NCBI data repository : https://www.ncbi.nlm.nih.gov/sra/PRJNA1274967. The original data are stored in the NCBI data repository for private peer review : https://www.ncbi.nlm.nih.gov/sra/PRJNA1274967.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study was approved by the Ethics Committee of Inner Mongolia Agricultural University under the approval number NND2017012.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statament\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eQin, J., Li, R., Raes, J., Arumugam, M., Burgdorf, K. 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On the limits of 16S rRNA gene-based metagenome prediction and functional profiling.\u003cem\u003eMicrobial Genomics\u003c/em\u003e,\u003cem\u003e10\u003c/em\u003e(2), 001203.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sympatric coexistence, gerbils, dietary habits, gut microbiome","lastPublishedDoi":"10.21203/rs.3.rs-7587564/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7587564/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe gut microbiota of mammals providesa range of benefits to their hosts; however, knowledge of interspecific differences in gut microecology remains limited. In this study, we employed 16S rRNA sequencing combined with metagenomic functional prediction to comparethe gut microbial composition and functional adaptability of two sympatrically distributed gerbil species with distinct diets—the herbivorous \u003cem\u003eRhombomys opimus\u003c/em\u003e ( RO) and the omnivorous \u003cem\u003eMeriones meridianu\u003c/em\u003es ( MM\u003cem\u003e \u003c/em\u003e). Our findings revealed that MM possessed greater microbial diversity, whereas RO exhibited a significant enrichment of norank_f__Muribaculaceae, a taxon associated with fibre degradation, along with a greaterabundance of genes involved in complex fibre breakdown. Notably, Lachnospiraceae_NK4A136_group and Desulfovibrio, which were markedly enriched in MM, may play pivotal roles in maintaining gut health and enhancing the efficiency of chitin degradation. Functional network analysis demonstrated that the cellulose-degrading gene networks in both gerbil species were predominantly synergistic; however, compared with RO, MM displayed significantly greater abundances of genes related to monosaccharide and chitin degradation. Further analysis indicated that the monosaccharide- and chitin-degrading gene networks of MM exhibited cooperative interaction patterns, whereas in RO, these networks were primarily antagonistic. This disparity reflects the heightened adaptability of MM to starch derived from plant seeds and chitin from insect exoskeletons, potentially linked to its omnivorous feeding habits and digestive constraints associated with smaller body size. Overall, this study advances our understanding of gut microecological adaptation mechanisms in rodents fed differentdiets and provides a valuable foundation for future research into the microbial ecology of wild rodents.\u003c/p\u003e","manuscriptTitle":"The gut microecology of the two sympatric gerbil species varies with diet","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-14 17:46:50","doi":"10.21203/rs.3.rs-7587564/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ce54cc74-0f13-4923-a362-061112b57751","owner":[],"postedDate":"October 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-30T13:23:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-14 17:46:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7587564","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7587564","identity":"rs-7587564","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}